Abstract
Protein kinases constitute the second largest class of drug targets, most prominently in cancer therapy. In this work, we focus on type II kinase inhibitors that bind to the "classical" inactive conformation. We analyze binding affinities of 50 type II inhibitors across 348 kinases, combining earlier results for 16 inhibitors (the "Davis data set") with recently obtained measurements for the remaining 34 (the "Schrödinger data set"). Using a Potts statistical energy model, we investigate the role of protein conformational reorganization in kinase selectivity and find that protein reorganization makes a large contribution to the selectivity (ROC AUC ∼0.8). We compare Potts threading predictions for the binding of 50 type II inhibitors to 348 kinases with those of DeepDTAGen, a sequence-based model trained on the Davis data set containing both type I and type II kinase inhibitors. DeepDTAGen performs well for the 16 inhibitors in the Davis data set but poorly for the remaining 34 in the Schrödinger data set, representing unseen data.